Horticulturae, Vol. 12, Pages 38: GBDR-Net: A YOLOv10-Derived Lightweight Model with Multi-Scale Feature Fusion for Accurate, Real-Time Detection of Grape Berry Diseases

Horticulturae doi: 10.3390/horticulturae12010038

Authors:
Pan Li
Jitao Zhou
Huihui Sun
Penglin Li
Xi Chen

Grape berries are highly susceptible to diseases during growth and harvest, which severely impacts yield and postharvest quality. While rapid and accurate disease detection is essential for real-time control and optimized management, it remains challenging due to complex symptom patterns, occlusions in dense clusters, and orchard environmental interference. Although deep learning presents a viable solution, robust methods specifically for detecting grape berry diseases under dense clustering conditions are still lacking. To bridge this gap, we propose GBDR-Net—a high-accuracy, lightweight, and deployable model based on YOLOv10. It incorporates four key enhancements: (1) an SDF-Fusion module replaces the original C2f module in deeper backbone layers to improve global context and subtle lesion feature extraction; (2) an additional Detect-XSmall head is integrated at the neck, with cross-concatenated outputs from SPPF and PSA modules, to enhance sensitivity to small disease spots; (3) the nearest-neighbor upsampling is substituted with a lightweight content-aware feature reassembly operator (LCFR-Op) for efficient and semantically aligned multi-scale feature enhancement; and (4) the conventional bounding box loss function is replaced with Inner-SIoU loss to accelerate convergence and improve localization accuracy. Evaluated on the Grape Berry Disease Visual Analysis (GBDVA) dataset, GBDR-Net achieves a precision of 93.4%, recall of 89.6%, mAP@0.5 of 90.2%, and mAP@0.5:0.95 of 86.4%, with a model size of only 4.83 MB, computational cost of 20.5 GFLOPs, and a real-time inference speed of 98.2 FPS. It outperforms models such as Faster R-CNN, SSD, YOLOv6s, and YOLOv8s across key metrics, effectively balancing detection accuracy with computational efficiency. This work provides a reliable technical solution for the intelligent monitoring of grape berry diseases in horticultural production. The proposed lightweight architecture and its design focus on dense, small-target detection offer a valuable framework that could inform the development of similar systems for other cluster-growing fruits and vegetables.

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Pan Li www.mdpi.com